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An end-to-end instance segmentation method based on improved ConvNeXt V2

International Journal of Wireless and Mobile Computing 2024年第4期26卷 384-392页

作者： Wang, Wenlu Sun, Ying Xu, Manman Bai, Dongxu Huang, Li Zou, Chunlong Chen, Baojia Tang, Dalai Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China College of Mechanical Engineering Hubei University of Automotive Technology Hubei Shiyan China Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance China Three Gorges University Hubei Yichang China School of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot China

In order to improve the efficiency of indoor mobile robots in locating and segmenting environmental instances, an instance segmentation method based on RTMDet is proposed. Firstly, the more powerful ConvNeXt V2 is used as the backbone network of the model, which improves the performance of the existing RTMDet model. Subsequently, NAS-FPN is used as a converged network to detect the network at any given time. Finally, AdamW is used as the model optimiser, which effectively solves the problem of excessive memory occupation when updating parameters and improves the performance of the model. The proposed method obtains the highest evaluation index in all cases. After training and testing on the Cityscapes data set, the recognition accuracy reaches 65.0%. © 2024 Inderscience Enterprises Ltd.

关键词： Statistical tests

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Automated heart disease prediction using improved explainable learning-based technique

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Neural Computing and Applications 2024年第26期36卷 16289-16318页

作者： Bizimana, Pierre Claver Zhang, Zuping Hounye, Alphonse Houssou Asim, Muhammad Hammad, Mohamed El-Latif, Ahmed A. Abd School of Computer Science and Engineering Central South University Changsha410083 China School of Mathematics and Statistics Central South University Changsha410008 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt Information Technology Department Faculty of Computers and Information Menoufia University Shebin El-Koom Egypt

Heart disease (HD) stands as a major global health challenge, being a predominant cause of death and demanding intricate and costly detection methods. The widespread impact of heart failure, contributing to increased rates of morbidity and mortality, underscores the urgency for accurate and timely prediction and diagnosis. This is crucial for effective prevention, early detection, and treatment, thereby reducing the threat to individual health. However, the early and precise prediction of HD remains a significant challenge. The complexity of medical data poses a considerable challenge for healthcare professionals, who are required to interpret and utilize this information swiftly for effective intervention. Addressing this gap, our study introduces a novel Improved Explainable Learning-Based Technique (IELBT) for HD prediction. This technique harnesses a strategic combination of feature selection, Venn diagrams, data normalization methods, optimized parameters, and machine learning algorithms, specifically tailored for predicting HD. We evaluated the performance of our model using the Alizadeh Sani HD dataset, aiming to accurately detect the presence or absence of the condition. Our results demonstrate that the IELBT, employing a support vector machine with a robust scaling approach, optimal parameterization, and a data split ratio of 70:30, achieves an impressive accuracy rate of 96.00%. Beyond achieving high accuracy, the IELBT outperforms similar models in existing literature and provides significant interpretability and explanation, essential elements in the field of medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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Geographical information Visualization on a Panoramic Sphere in an Immersive Environment

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Journal of Ocean University of China 2023年第4期22卷 961-974页

作者： MA Ying TIAN Fenglin LIU Wen TANG Moran LI Jinyu CHEN Ge Frontiers Science Center for Deep Ocean Multispheres and Earth System School of Marine TechnologyOcean University of ChinaQingdao 266100China Laboratory for Regional Oceanography and Numerical Modeling Laoshan LaboratoryQingdao 266100China Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

In this paper,we propose a novel approach to visualizing global geographical information:a panoramic sphere in an immersive *** whole geographical surface can be observed through the rotating of heads as the viewpoint of the panoramic sphere is inside the *** compared three approaches to visualizing the earth for rendering the geographical information in a virtual reality *** the tasks of terrestrial and marine geographical information,we compare the visualization effects on a)a globe,b)a flat map and c)a panoramic *** geographical information tasks include the area comparison and direction *** geographical information tasks contain the visualization of sea surface temperature and sea surface *** terrestrial geographical information tasks,the experimental results show that the panoramic sphere outperforms the globe and the flat map,with a higher average accuracy and a shorter *** marine geographical information task,the panoramic sphere visualization is also superior to the flat map and the globe in an immersive environment for the sea surface temperature data and the sea surface current *** all three visualization experiments,the panoramic sphere is most preferred by the participants,particularly for global,transcontinental and transoceanic needs.

关键词： the panoramic sphere immersive environment virtual reality oceanographic information visualization

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Augmented Genetic Algorithm for Decoupling Capacitor Optimization in Power Distribution Network Design Through Improved Population Generation

IEEE Transactions on Signal and Power Integrity

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IEEE Transactions on Signal and Power Integrity 2024年 3卷 186-198页

作者： Juang, Jack Zhang, Ling Manoharan, Haran De Paulis, Francesco Hwang, Chulsoon Missouri University of Science and Technology Electromagnetic Compatibility Lab RollaMO65401 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of L'Aquila UAq Electromagnetic Compatibility Lab L'Aquila67100 Italy

In power distribution network designs, a large number of decoupling capacitors (decaps) may be needed to satisfy target impedance limits. Many algorithms have been proposed and implemented for finding the optimal decap placement, including genetic algorithms (GA), and machine learning methods. In this work, an improved GA is proposed for finding the decap placement pattern that can satisfy a target impedance using the minimum number of decaps. The distribution of capacitors expected to appear in the global minimum solution is first predicted by determining how effective each decap type is toward satisfying certain critical impedance points. This estimation is used to inform the generation of initial solutions in order to put the initial search space nearer the global minimum and ensure certain solution characteristics appear. GA search using this improved population generation is found to be an improvement over a Canonical GA implementation, by finding solutions where the latter could not, or finding a solution using fewer decaps. © 2024 The Authors.

关键词： Capacitor bank

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection systems

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Computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

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Computers, Materials & Continua 2023年第1期74卷 363-378页

作者： Khalid Hamid Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Due to a tremendous increase in mobile traffic,mobile operators have started to restructure their networks to offload their *** directions will lead to fundamental changes in the design of future Fifthgeneration(5G)cellular *** the formal reason,the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph *** number that can be uniquely calculated by a graph is known as a graph *** the last two decades,innumerable numerical graph invariants have been portrayed and used for correlation *** any case,no efficient assessment has been embraced to choose,how much these invariants are connected with a network *** paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical *** K-banhatti sombor invariants(KBSO)and Contrharmonic-quadratic invariants(CQIs)are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or *** the hexagonal networks are used in mobile base stations in layered,forms called *** review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced *** deduced outcomes can be utilized for the modeling of mobile cellular networks,multiprocessors interconnections,microchips,chemical compound synthesis and memory interconnection *** results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network(MBSN)for the high load of traffic and minimal traffic also.

关键词： Hexagonal networks k-banhatti sombor invariants maple network graph artificial intelligence contraharmonic-quadratic invariants honeycomb

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Natural Sit-to-Stand Control of a Musculoskeletal Model with Deep Reinforcement Learning-Based Reward Shaping

Natural Sit-to-Stand Control of a Musculoskeletal Model with...

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2024 China Automation Congress, CAC 2024

作者： Dong, Borui Chen, Yaru Chen, Wenqian Wang, Yongxuan Wang, Xiaohong Liu, Rong Liaoning Key Lab of IC BME System School of Biomedical Engineering Dalian University of Technology Dalian China School of Information Engineering Dalian University Dalian China Department of Neuroelectrophysiology Central Hospital of Dalian University of Technology Dalian China

ISBN: (纸本)9798350368604

Understanding the neuromuscular control mechanisms underlying the human sit-to-stand (STS) movement, a fundamental daily activity, is crucial for enhancing the physical capabilities of older adults and advancing applications in robotics and exoskeletons. This paper introduces a deep reinforcement learning (DRL) approach with reward shaping to replicate natural human STS movements using a lower limb musculoskeletal model with three degrees of freedom and nine muscles. We used proximal policy optimization (PPO) with two behavior-guided reward shaping functions,: one focused on joint angle changes (Rjoint) and the other on head height changes (Rhead), each tailored to the model’s capabilities and the movement’s objectives. To assess the effectiveness of our control method, we collected kinematic and kinetic data of human STS movements, allowing for a comparison of joint angles between the musculoskeletal model and human subjects. Both reward conditions demonstrated a strong correlation with experimental data, with the Rhead reward achieving a correlation of 0.9 and faster convergence compared to Rjoint. Additionally, Rhead more accurately captured human joint dynamics, enhancing the realism of the simulated movements, while Rjoint proved more effective in reducing energy consumption. These findings highlight the potential of DRL with reward shaping to generate realistic and efficient movement patterns, paving the way for future applications in robotics and assistive technologies. © 2024 IEEE.

关键词： Deep reinforcement learning

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Deep Learning Techniques for Advanced Large-Scale Data Processing 15

Deep Learning Techniques for Advanced Large-Scale Data Proce...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sunil, M.P. Shekhawat, Jyoti Vanaja, T. Mohammed, Vazeer Ali Thaware, Shailesh J. Singh, Vaishali Karnataka Bangalore India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Department of Computer & Information System Lewis University Illinois United States Department of Computer Engineering Vishwakarma Institute of Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350370249

Deep getting to know techniques have become increasingly more popular due to their potential to improve huge-scale records processing performance. Leveraging specialized hardware which includes GPUs, deep getting to know algorithms can attain mind-blowing pace will increase compared to traditional algorithms. further, deep gaining knowledge of fashions can discover complicated non-linear relationships in big datasets, permitting greater accurate overall performance and less fake predictions. Latest advances in deep mastering have enabled it to be implemented to remedy a plethora of information processing duties, from photo recognition to natural language processing. CNNs, RNNs, and GANs are a number of the maximum famous deep gaining knowledge of fashions used in tackling complex data processing obligations. agencies consisting of Google, fb, and Amazon use these sophisticated frameworks to build predictive models that help improve numerous programs. Deep getting to know strategies offer exquisite ability to take massive-scale information processing to the following stage. corporations are increasingly more becoming privy to the emerging opportunities, and actively investing in deep mastering to liberate the untapped strength of their information. ©2024 IEEE.

关键词： Large datasets

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TERMS: Task management policies to achieve high performance for mixed workloads using surplus resources

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Journal of Parallel and Distributed Computing 2022年 170卷 74-85页

作者： Yu, Jinyu Tong, Wei Lv, Pengze Feng, Dan Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology School of Computer Science and Technology Huazhong University of Science and Technology Ministry of Education of China Hubei Wuhan China

Resource contentions and performance interferences can lead to workload performance degradation in mixed-workload deployment clusters. Previous work guarantees the resource requirements of latency-sensitive tasks and reduces performance losses to batch jobs by reclaiming surplus resources from over-provisioned tasks. While the fragmentation of resources leads to a mismatch between provisioned resources and task requirements, resulting in high operation overheads and losses of task fairness. This paper proposes TERMS, the task management policies based on task relevance, resource distribution, and task fairness to achieve efficient and low-cost task management. TERMS mainly includes three types of management policies. The task scheduling policy can schedule new tasks according to task relevance. Task selection strategies select tasks for resource provisioning and task resumption based on resource requirements and task fairness. If necessary, the node selection strategy can be used to choose befitting target nodes based on task relevance and node resource information for task migration when eliminating straggler tasks. Evaluation results show that TERMS can further improve the performance of latency-sensitive services and batch jobs, reduce management overheads, and avoid operation failures. © 2022 Elsevier Inc.

关键词： Scheduling

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Dispatcher: Resource-aware Nakamoto Blockchain via Hierarchical Topology and Adaptive Incentives

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Distributed Ledger Technologies: Research and Practice 2024年第2期3卷 1-20页

作者： Hai Jin Shuohua Dong Xiaohai Dai Yuandi Cai Jiang Xiao National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Mainstream blockchain systems such as Bitcoin and Ethereum are revolutionizing the financial industry by adopting the Nakamoto consensus protocol, i.e., Proof-of-Work (PoW). Only nodes with sufficient computing resources can work out the PoW difficulties, thereby increasing the mining cost of malicious attackers and ensuring the security of blockchain systems. Such an assumption of having abundant resources leads to drawbacks of low throughput and risk of centralization. In this article, we present Dispatcher, a novel distributed consensus protocol that takes resource heterogeneity into account to ensure resource-aware PoW with high efficiency. Dispatcher introduces a hierarchical topology to offer flexible PoW difficulties tailored for different nodes’ resources. In particular, it utilizes the limited resource of each node to jointly maximize the performance by concurrent mining. Moreover, we design an adaptive incentive mechanism to fit the available resource of blockchain nodes to rewards. Our experiments show that Dispatcher enjoys a substantial performance margin over the state-of-the-art. We can achieve a 50% throughput improvement compared with OHIE.

关键词： Blockchain resource heterogeneity incentive hierarchical architecture

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